streamhacker.eth

I make stuff with words

[streamhacker.eth @japerk]

@grok how plausible is this? What’s the likely time frame?

[streamhacker.eth @japerk]

First comes the data centers then comes nuclear.

How many years before the strain on the electric grid creates the forcing function for local nuclear power?

Mandate electric cars. Get rid of gas stoves & water heaters. Build data centers. Close existing nuclear power plants. Try to push for solar and offshore wind even though everyone knows they’re not reliable. So what’s the only remaining option?

Are the companies building out the data centers already planning for local nuclear power? Do the nearby cities know?

Nuclear makes great sense and the US needs cheaper more reliable electricity, but not sure even YIMBYs want a nuclear power plant in their backyard.

[Addy Osmani @addyosmani]

https://t.co/0DyFIXJueI

[Kyle 🚄 @KyleTrainEmoji]

PICARD: Data, shields up

DATA: Brilliant! Shields can reduce damage we sustain. Not immunity. Not hubris. Just prudence. It's not precaution—it's strategy.

[camera shakes]

WORF: HULL BREACHES ON NINE DECKS

DATA: Here's what happened: you told me to raise shields, and I didn't

[Shub @shub0414]

Most valuable thing | learned from a senior engineer:

How to read a codebase you've never seen.

1. Find where requests come in
2. Follow one path end to end
3. Map the data flow, ignore the logic
4. Only then zoom into the details

Took them 10 minutes to teach. Saved me years of fumbling.

Some skills are so fundamental we forget they need to be taught explicitly.

[gfodor.id @gfodor]

Expecting agents to produce correct, fast, and clean code at the same time is probably the biggest pitfall developers are falling into. Do each in sequence, ensuring you lock-in gains with a harness and tests between each step, and you can get great results.

[streamhacker.eth @japerk]

Course idea: how to effectively swear at LLMs for fun & profit. Learn which swear words and insults work best for each model to make it actually do the thing you want.

[Mitchell Hashimoto @mitchellh]

Fork your dependencies, trim them to only your use case, never update unless it breaks for your users. I’ve been vocal about this for 10+ years. I’ve always said that updating is way riskier than latent bugs (which can be tracked and CVEs monitored).

If you are updating a dependency, it’s on you to analyze every single commit in the full transitive set of dependencies. If you dont see anything compelling, dont update!

I remember at HashiCorp once in awhile an engineer would try to update a dep or replace a DIY lib with an external one and id always ask “show me the commit we need.” Dont update for the sake of it.

Feeling pretty swell about this mentality with all the supply chain attacks happening.

[streamhacker.eth @japerk]

apparently if you edit `.openclaw/openclaw.json` yourself, @openclaw will just overwrite with whatever config you had before. And despite what `openclaw configure --section model` says about `Updated ~/.openclaw/openclaw.json` it literally doesn't

[streamhacker.eth @japerk]

Meanwhile @openclaw gets a 500 & 400 error from @ollama POST /api/chat but doesn't show any error logs or problems, just spins doing nothing while ollama pins the CPU until it gives up and unloads all the models.

[streamhacker.eth @japerk]

I tell @openclaw to use a local @ollama model then watch `ollama ps` and it's showing a different model? Then a previous model I tried? Anything but the actual model I told it to use. And @perplexity_ai recommends models that don't fit in RAM in or can't use tools. 🤡🤷‍♂️

[kache @yacineMTB]

you still need to read and understand code

[DFINITY Foundation @dfinity]

AI agents are powerful — but they weren't trained on ICP.

ICP Skills gives them real knowledge — so they can build sovereign software on an onchain open cloud that's tamperproof, unstoppable, and handles digital assets and payments.

https://t.co/ZVBLuvnbTA

[AmSpeed⚡️ @devdaryl]

🚨 What’s wild is the actual World Computer ( Internet Computer Protocol ) launched in 2021 and most people act like it doesn’t exist 🤔

Now we have users who know nothing about blockchain building apps on the Internet Computer through chatting with AI thanks to @caffeineai 🔥

[streamhacker.eth @japerk]

@BobBodily Also Zero runs EVM, while ICP runs WASM canisters which are far more capable

[streamhacker.eth @japerk]

@BobBodily Also Zero runs EVM, while ICP runs WASM canisters which are far more capable

[Addy Osmani @addyosmani]

Every time we've made it easier to write software, we've ended up writing exponentially more of it.

When high-level languages replaced assembly, programmers didn't write less code - they wrote orders of magnitude more, tackling problems that would have been economically impossible before. When frameworks abstracted away the plumbing, we didn't reduce our output - we built more ambitious applications. When cloud platforms eliminated infrastructure management, we didn't scale back - we spun up services for use cases that never would have justified a server room.

@levie recently articulated why this pattern is about to repeat itself at a scale we haven't seen before, using Jevons Paradox as the frame. The argument resonates because it's playing out in real-time in our developer tools. The initial question everyone asks is "will this replace developers?" but just watch what actually happens. Teams that adopt these tools don't always shrink their engineering headcount - they expand their product surface area. The three-person startup that could only maintain one product now maintains four. The enterprise team that could only experiment with two approaches now tries seven.

The constraint being removed isn't competence but it's the activation energy required to start something new. Think about that internal tool you've been putting off because "it would take someone two weeks and we can't spare anyone"? Now it takes three hours. That refactoring you've been deferring because the risk/reward math didn't work? The math just changed.

This matters because software engineers are uniquely positioned to understand what's coming. We've seen this movie before, just in smaller domains. Every abstraction layer - from assembly to C to Python to frameworks to low-code - followed the same pattern. Each one was supposed to mean we'd need fewer developers. Each one instead enabled us to build more software.

Here's the part that deserves more attention imo: the barrier being lowered isn't just about writing code faster. It's about the types of problems that become economically viable to solve with software. Think about all the internal tools that don't exist at your company. Not because no one thought of them, but because the ROI calculation never cleared the bar. The custom dashboard that would make one team 10% more efficient but would take a week to build. The data pipeline that would unlock insights but requires specialized knowledge. The integration that would smooth a workflow but touches three different systems.

These aren't failing the cost-benefit analysis because the benefit is low - they're failing because the cost is high. Lower that cost by "10x", and suddenly you have an explosion of viable projects. This is exactly what's happening with AI-assisted development, and it's going to be more dramatic than previous transitions because we're making previously "impossible" work possible.

The second-order effects get really interesting when you consider that every new tool creates demand for more tools. When we made it easier to build web applications, we didn't just get more web applications - we got an entire ecosystem of monitoring tools, deployment platforms, debugging tools, and testing frameworks. Each of these spawned their own ecosystems. The compounding effect is nonlinear.

Now apply this logic to every domain where we're lowering the barrier to entry. Every new capability unlocked creates demand for supporting capabilities. Every workflow that becomes tractable creates demand for adjacent workflows. The surface area of what's economically viable expands in all directions.

For engineers specifically, this changes the calculus of what we choose to work on. Right now, we're trained to be incredibly selective about what we build because our time is the scarce resource. But when the cost of building drops dramatically, the limiting factor becomes imagination, "taste" and judgment, not implementation capacity. The skill shifts from "what can I build given my constraints?" to "what should we build given that constraints have in some ways been evaporated?"

The meta-point here is that we keep making the same prediction error. Every time we make something more efficient, we predict it will mean less of that thing. But efficiency improvements don't reduce demand - they reveal latent demand that was previously uneconomic to address. Coal. Computing. Cloud infrastructure. And now, knowledge work.

The pattern is so consistent that the burden of proof should shift. Instead of asking "will AI agents reduce the need for human knowledge workers?" we should be asking "what orders of magnitude increase in knowledge work output are we about to see?"

For software engineers it's the same transition we've navigated successfully several times already. The developers who thrived weren't the ones who resisted higher-level abstractions; they were the ones who used those abstractions to build more ambitious systems. The same logic applies now, just at a larger scale.

The real question is whether we're prepared for a world where the bottleneck shifts from "can we build this?" to "should we build this?" That's a fundamentally different problem space, and it requires fundamentally different skills.

We're about to find out what happens when the cost of knowledge work drops by an order of magnitude. History suggests we (perhaps) won't do less work - we'll discover we've been massively under-investing in knowledge work because it was too expensive to do all the things that were actually worth doing.

The paradox isn't that efficiency creates abundance. The paradox is that we keep being surprised by it.

[Steven Pinker @sapinker]

Bombshell: Oliver Sacks (a humane man & a fine essayist) made up many of the details in his famous case studies, deluding neuroscientists, psychologists, & general readers for decades. The man who mistook his wife for a hat? The autistic twins who generated multi-digit prime numbers? The institutionalized, paralyzed man who tapped out allusions to Rilke? Made up to embellish the stories. Probably also: the aphasic patients who detected lies better than neurologically intact people, including Ronald Reagan's insincerity. https://t.co/77nQRF8kp6

[Tim Dettmers @Tim_Dettmers]

My new blog post discusses the physical reality of computation and why this means we will not see AGI or any meaningful superintelligence: https://t.co/jsAKQ6T3gC

[François Chollet @fchollet]

If a problem seems intractable, it's almost always because your specification of it is vague or incomplete.

The solution doesn't appear when you "think harder". It appears when you describe the problem in a sufficiently precise and explicit fashion -- until you see its true form. A well-specified problem has a small, legible solution space.